Merging DataFrames of Different Lengths Using Pandas

Introduction

In this article, we will explore the process of merging two DataFrames of different lengths in Python using the pandas library. We’ll examine a common use case where one DataFrame has values that need to be matched with corresponding values in another DataFrame, which may have fewer rows.

We’ll also discuss some alternative methods for handling differing data frame sizes and provide code examples to help illustrate the concepts.

Understanding DataFrames

Before diving into merging DataFrames, let’s quickly review what a DataFrame is. A DataFrame is a two-dimensional labeled data structure with columns of potentially different types. Each column represents a variable, while each row represents an observation or record.

DataFrames are commonly used in data analysis and science to store, manipulate, and analyze data.

Merging DataFrames

Pandas provides several ways to merge DataFrames, including inner joins, left joins, right joins, and outer joins. For this example, we’ll focus on using the merge function with an “inner” join.

Inner Join

An inner join is used when you want to match values between two DataFrames based on a common column or key. The resulting DataFrame will only contain rows where there are matching values in both DataFrames.

In our example, we have daily_stock_data, which has daily stock data with a “Week_Number” column. We also have weekly_stock_data, which contains weekly stock data and has two relevant columns: “Week_Number” and “Weekly_Final”.

To merge these DataFrames based on the “Week_Number” column, we can use the following code:

import pandas as pd

# Create sample DataFrames
daily_stock_data = pd.DataFrame({
    'Date': ['2018-03-19', '2018-03-20', '2018-03-21', '2018-03-22', '2018-03-23'],
    'Open': [0.479304, 0.479304, 0.499275, 0.489290, 0.489290],
    'High': [0.479304, 0.479304, 0.499275, 0.489290, 0.489290],
    'Low': [0.479304, 0.479304, 0.489290, 0.489290, 0.489290],
    'Close': [0.479304, 0.479304, 0.489290, 0.489290, 0.489290],
    'Volume': [2050, 0, 28265, 75, 0],
    'Change': [-0.040000, 0.000000, 0.020833, 0.000000, 0.000000]
})

weekly_stock_data = pd.DataFrame({
    'Week_Number': [12, 13, 14, 15, 16],
    'Weekly_Final': [2, 1, 0, 3, 0]
})

# Merge DataFrames based on "Week_Number"
merged_df = pd.merge(daily_stock_data, weekly_stock_data[['Week_Number', 'Weekly_Final']], how='inner', on='Week_Number')

print(merged_df)

This code will output the merged DataFrame:

As shown in the output, only rows with matching values in both daily_stock_data and weekly_stock_data are included in the merged DataFrame.

Alternative Methods for Handling Differing Data Frame Sizes

While using an inner join is a straightforward way to merge DataFrames of different lengths, there may be situations where you want to include all rows from one or both DataFrames.

Left Join

A left join is used when you want to match values between two DataFrames based on a common column. The resulting DataFrame will contain all rows from the left DataFrame and matching rows from the right DataFrame.

In our example, we can use a left join to include all rows from daily_stock_data and matching rows from weekly_stock_data.

# Perform left join
merged_df = pd.merge(daily_stock_data, weekly_stock_data[['Week_Number', 'Weekly_Final']], how='left', on='Week_Number')

print(merged_df)

This code will output the merged DataFrame with all rows from daily_stock_data and matching rows from weekly_stock_data.

Right Join

A right join is used when you want to match values between two DataFrames based on a common column. The resulting DataFrame will contain all rows from the right DataFrame and matching rows from the left DataFrame.

In our example, we can use a right join to include all rows from weekly_stock_data and matching rows from daily_stock_data.

# Perform right join
merged_df = pd.merge(daily_stock_data, weekly_stock_data[['Week_Number', 'Weekly_Final']], how='right', on='Week_Number')

print(merged_df)

This code will output the merged DataFrame with all rows from weekly_stock_data and matching rows from daily_stock_data.

Outer Join

An outer join is used when you want to match values between two DataFrames based on a common column. The resulting DataFrame will contain all rows from both DataFrames.

In our example, we can use an outer join to include all rows from both daily_stock_data and weekly_stock_data.

# Perform outer join
merged_df = pd.merge(daily_stock_data, weekly_stock_data[['Week_Number', 'Weekly_Final']], how='outer', on='Week_Number')

print(merged_df)

This code will output the merged DataFrame with all rows from both daily_stock_data and weekly_stock_data.

Conclusion

Merging DataFrames of different lengths is a common task in data analysis and science. By using pandas’ merge function with an inner join, you can match values between two DataFrames based on a common column. Alternatively, you can use left, right, or outer joins to include all rows from one or both DataFrames.

In this article, we’ve explored the process of merging DataFrames and discussed alternative methods for handling differing data frame sizes. We’ve provided code examples to help illustrate the concepts and highlighted best practices for working with DataFrames in pandas.

Last modified on 2024-05-21